Exploring Candida auris nosocomial outbreaks with Solu Platform

Abstract

We used Solu Platform to analyze a dataset of 21 Candida auris samples. The platform successfully identified the species and resistance genes of each sample. It also detected three potential outbreaks, which were in align with previous findings by Welsh et al. (2021).

Introduction

Candida auris, a recently identified fungal pathogen, poses significant challenges to healthcare due to its multidrug resistance and associated high mortality rates [1]. Furthermore, traditional phenotypic and molecular techniques struggle to trace Candida auris transmissions and differentiate the species [2].

In order to address these challenges, whole-genome sequencing (WGS) of pathogens can be applied to provide a deeper understanding of the species, infection sources, and transmission routes [3]. Solu Platform (Solu Healthcare Oy, Helsinki) provides a user-friendly approach to microbial DNA analysis. The platform enables accurate detection of species, antimicrobial resistance genes, and possible outbreaks in healthcare environments. In this case study, we examine a dataset of 21 Candida auris samples collected by Welsh et al. (2021) [4] and investigate whether novel bioinformatic methods can shed light on nosocomial outbreaks.

Dataset

The dataset we used in this case study was gathered by Welsh et al. in their 2021 paper. The study aimed to create an outbreak benchmark dataset to ensure agreement with the analysis results of genomic surveillance providers. The dataset includes 23 Candida auris samples, which represent clade I. Excluding the two reference samples, we considered a total of 21 samples in this study.

Methods

We downloaded the raw reads for the samples from the European Nucleotide Archive (ENA) at EMBL-EBI, under accession number PRJNA493622. We then uploaded the reads to the Solu Platform. The platform also supports direct downloads of reads from ENA using the SRA accession number, accelerating the data logistics process.

The platform automatically assembled the reads, identified the species, clade, and antifungal resistance genes, and executed a reference-based phylogeny pipeline. This pipeline also clusters closely related samples together, indicating a potential outbreak. For further information about the platform's methodology, please visit our methodology description.

Results

Species and antifungal resistance

The platform consistently identified all samples as C. auris clade I, aligning with the findings of Welsh et al. All samples also contained the AFR mutations ERG11_K143R and CDR1_V704L, which confer resistance to anidulafungin, azoles, flucytosine, and micafungin [5]. Welsh et al. did not examine the antifungal resistance of the samples in their study.

Phylogenetic tree and clusters

The platform generates a phylogenetic tree and detects clusters of samples. It assigns samples to clusters when they have an SNP distance of less than 20, implying close evolutionary proximity.

The tree image below shows the three detected clusters: Ca1, Ca2, and Ca3, containing 13, 5, and 3 samples respectively. The phylogenetic tree is in agreement with an SNP distance-based clustering and clearly shows the evolutionary basis for three distinct clusters.

The three clusters, containing 21 isolates, were also identified in the Welsh et al. study.

Transmission reconstruction

We reconstructed the most likely transmission route for cluster Ca1 using Solu’s transmission inference algorithm (beta feature). The samples within the cluster Ca1 were collected from three different locations as shown in the table below.

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The algorithm grouped the samples collected from the same locations to same sublineages. A more detailed analysis of the sublineages allowed for a greater understanding of the transmission chain. For instance, it revealed that sample B12352, despite being collected in the same year and location as B12490, is not a direct descendant of B12490 (refer to the video). Their mutual sublineage diverged years before the sampling.

It's important to note that Welsh et al. only reported the collection year of the samples, introducing some ambiguity into the transmission timeline. To mitigate this, the algorithm estimated the most probable sampling dates based on the mutation count in the samples.

Discussion

This case study showcases how the Solu Platform can effectively analyze genomic data and pinpoint potential Candida auris outbreaks. The platform successfully identified the species, clade, and resistance genes of the samples and delivered a comprehensive phylogenetic analysis.

The results aligned with Welsh et al.'s findings, emphasizing the importance of whole-genome sequencing in tracing transmissions and distinguishing species. It demonstrated that the Solu Platform is a useful resource for healthcare settings managing potential outbreaks of multidrug-resistant pathogens such as Candida auris.

References

1. Du H, Bing J, Hu T, Ennis CL, Nobile CJ, Huang G. Candida auris: Epidemiology, biology, antifungal resistance, and virulence. Xue C, editor. PLoS Pathog. 2020 Oct 22;16(10):e1008921.
2. Jeffery-Smith A, Taori SK, Schelenz S, Jeffery K, Johnson EM, Borman A, et al. Candida auris: a Review of the Literature. Clin Microbiol Rev. 2018 Jan;31(1):e00029-17.
3. Eyre DW. Infection prevention and control insights from a decade of pathogen whole-genome sequencing. J Hosp Infect. 2022;122:180-186. doi:10.1016/j.jhin.2022.01.024
4. Welsh RM, Misas E, Forsberg K, Lyman M, Chow NA. Candida auris Whole-Genome Sequence Benchmark Dataset for Phylogenomic Pipelines. Journal of Fungi. 2021; 7(3):214. https://doi.org/10.3390/jof7030214
5. Jain A, Singhal N, Kumar M. AFRbase: a database of protein mutations responsible for antifungal resistance. Martelli PL, editor. Bioinformatics. 2023 Nov 1;39(11):btad677.